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Module « numpy.matlib »

Fonction isin - module numpy.matlib

Signature de la fonction isin

def isin(element, test_elements, assume_unique=False, invert=False, *, kind=None) 

Description

help(numpy.matlib.isin)

Calculates ``element in test_elements``, broadcasting over `element` only.
Returns a boolean array of the same shape as `element` that is True
where an element of `element` is in `test_elements` and False otherwise.

Parameters
----------
element : array_like
    Input array.
test_elements : array_like
    The values against which to test each value of `element`.
    This argument is flattened if it is an array or array_like.
    See notes for behavior with non-array-like parameters.
assume_unique : bool, optional
    If True, the input arrays are both assumed to be unique, which
    can speed up the calculation.  Default is False.
invert : bool, optional
    If True, the values in the returned array are inverted, as if
    calculating `element not in test_elements`. Default is False.
    ``np.isin(a, b, invert=True)`` is equivalent to (but faster
    than) ``np.invert(np.isin(a, b))``.
kind : {None, 'sort', 'table'}, optional
    The algorithm to use. This will not affect the final result,
    but will affect the speed and memory use. The default, None,
    will select automatically based on memory considerations.

    * If 'sort', will use a mergesort-based approach. This will have
      a memory usage of roughly 6 times the sum of the sizes of
      `element` and `test_elements`, not accounting for size of dtypes.
    * If 'table', will use a lookup table approach similar
      to a counting sort. This is only available for boolean and
      integer arrays. This will have a memory usage of the
      size of `element` plus the max-min value of `test_elements`.
      `assume_unique` has no effect when the 'table' option is used.
    * If None, will automatically choose 'table' if
      the required memory allocation is less than or equal to
      6 times the sum of the sizes of `element` and `test_elements`,
      otherwise will use 'sort'. This is done to not use
      a large amount of memory by default, even though
      'table' may be faster in most cases. If 'table' is chosen,
      `assume_unique` will have no effect.


Returns
-------
isin : ndarray, bool
    Has the same shape as `element`. The values `element[isin]`
    are in `test_elements`.

Notes
-----
`isin` is an element-wise function version of the python keyword `in`.
``isin(a, b)`` is roughly equivalent to
``np.array([item in b for item in a])`` if `a` and `b` are 1-D sequences.

`element` and `test_elements` are converted to arrays if they are not
already. If `test_elements` is a set (or other non-sequence collection)
it will be converted to an object array with one element, rather than an
array of the values contained in `test_elements`. This is a consequence
of the `array` constructor's way of handling non-sequence collections.
Converting the set to a list usually gives the desired behavior.

Using ``kind='table'`` tends to be faster than `kind='sort'` if the
following relationship is true:
``log10(len(test_elements)) >
(log10(max(test_elements)-min(test_elements)) - 2.27) / 0.927``,
but may use greater memory. The default value for `kind` will
be automatically selected based only on memory usage, so one may
manually set ``kind='table'`` if memory constraints can be relaxed.

Examples
--------
>>> import numpy as np
>>> element = 2*np.arange(4).reshape((2, 2))
>>> element
array([[0, 2],
       [4, 6]])
>>> test_elements = [1, 2, 4, 8]
>>> mask = np.isin(element, test_elements)
>>> mask
array([[False,  True],
       [ True, False]])
>>> element[mask]
array([2, 4])

The indices of the matched values can be obtained with `nonzero`:

>>> np.nonzero(mask)
(array([0, 1]), array([1, 0]))

The test can also be inverted:

>>> mask = np.isin(element, test_elements, invert=True)
>>> mask
array([[ True, False],
       [False,  True]])
>>> element[mask]
array([0, 6])

Because of how `array` handles sets, the following does not
work as expected:

>>> test_set = {1, 2, 4, 8}
>>> np.isin(element, test_set)
array([[False, False],
       [False, False]])

Casting the set to a list gives the expected result:

>>> np.isin(element, list(test_set))
array([[False,  True],
       [ True, False]])

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